Social networks such as those facilitated by social media, online games, or mobile devices have attracted increasing attention from both academia and industry that explore how to leverage such networks for greater business and societal benefits. Toward that end, we develop novel models, theories, and methods that mine massive social network data for business purposes. In this project, we focus on a unique phenomenon in social networks – the diffusion of adoption behavior (e.g., adoption of a product, service, or opinion) from one social entity to another. Specifically, we investigate three critical and related problems concerning this phenomenon: adoption, persuasion, and link recommendation. That is, the diffusion of adoption behavior is initiated by persuaders and reached to adopters through the linkage structure of a social network. Accordingly, we study the following problems: how to predict adoption probabilities in a social network? how to predict top persuaders in a social network? and how to recommend links for a social network? Let us take the problem of predicting adoption probabilities as an illustration. Adoption probability refers to the probability that a social entity will adopt a product, service, or opinion in the foreseeable future. Building on relevant social network theories, we identify key factors that affect adoption decisions: social influence, structural equivalence, entity similarity, and hidden factors. The principal challenge thus is how to predict adoption probabilities in the presence of hidden factors that are generally unobserved. To address this challenge, we develop a Bayesian learning method on the basis of the expectation-maximization framework. Using data from two large-scale social networks, we demonstrate that the developed method significantly outperforms prevalent existing methods.